Construction and validation of a novel lysosomal signature for hepatocellular carcinoma prognosis, diagnosis, and therapeutic decision-making

Lysosomes is a well-recognized oncogenic driver and chemoresistance across variable cancer types, and has been associated with tumor invasiveness, metastasis, and poor prognosis. However, the significance of lysosomes in hepatocellular carcinoma (HCC) is not well understood. Lysosomes-related genes (LRGs) were downloaded from Genome Enrichment Analysis (GSEA) databases. Lysosome-related risk score (LRRS), including eight LRGs, was constructed via expression difference analysis (DEGs), univariate and LASSO-penalized Cox regression algorithm based on the TCGA cohort, while the ICGC cohort was obtained for signature validation. Based on GSE149614 Single-cell RNA sequencing data, model gene expression and liver tumor niche were further analyzed. Moreover, the functional enrichments, tumor microenvironment (TME), and genomic variation landscape between LRRSlow/LRRShigh subgroup were systematically investigated. A total of 15 Lysosomes-related differentially expressed genes (DELRGs) in HCC were detected, and then 10 prognosis DELRGs were screened out. Finally, the 8 optimal DELRGs (CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B) were selected to construct the LRRS prognosis signature of HCC. LRRS was considered as an independent prognostic factor and was associated with advanced clinicopathological features. LRRS also proved to be a potential marker for HCC diagnosis, especially for early-stage HCC. Then, a nomogram integrating the LRRS and clinical parameters was set up displaying great prognostic predictive performance. Moreover, patients with high LRRS showed higher tumor stemness, higher heterogeneity, and higher genomic alteration status than those in the low LRRS group and enriched in metabolism-related pathways, suggesting its underlying role in the progression and development of liver cancer. Meanwhile, the LRRS can affect the proportion of immunosuppressive cell infiltration, making it a vital immunosuppressive factor in the tumor microenvironment. Additionally, HCC patients with low LRRS were more sensitive to immunotherapy, while patients in the high LRRS group responded better to chemotherapy. Upon single-cell RNA sequencing, CLN3, GBA, and LAPTM4B were found to be specially expressed in hepatocytes, where they promoted cell progression. Finally, RT-qPCR and external datasets confirmed the mRNA expression levels of model genes. This study provided a direct links between LRRS signature and clinical characteristics, tumor microenvironment, and clinical drug-response, highlighting the critical role of lysosome in the development and treatment resistance of liver cancer, providing valuable insights into the prognosis prediction and treatment response of HCC, thereby providing valuable insights into prognostic prediction, early diagnosis, and therapeutic response of HCC.

www.nature.com/scientificreports/ The coefficient ( i ) and expression of gene ( i ) represented the coefficient obtained from LASSO analysis and the normalized expression value of gene ( i ), respectively.HCC samples were then split into two subgroups: high- LRRS group and low-LRRS group, according to the median value of the LRRS.Then, using R's survival package, survival probabilities were calculated.In addition, R package "stats" (version 3.6.0),"umap" (version 0.2.7.0), and "Rtsne" (version 0.15) were then performed respectively for principal component analysis (PCA), uniform manifold approximation and projection (UMAP) and t-statistic neighborhood embedding (tSNE) to illustrate the distribution of the two risk groups.The "timeROC" package was utilized to evaluate the prediction efficiency.

The association of LRRS with clinical features and diagnostic evaluation of the LRRS
In TCGA cohort, LRRS values were compared, and survival prognosis were analyzed under different stratification of clinical variables.These were further analyzed in the validation cohort.Then, univariate and multivariate Cox regression analyses were performed.For diagnosis, the LRRS levels in TCGA groups were firstly compared, and the receiver operating characteristic (ROC) curves were graphed to evaluate the diagnostic value of LRRS, especially for early diagnosis of HCC.Moreover, the AUCs of ROC were calculated to compare the diagnostic efficacy of the LRRS vs AFP in diagnosing HCC.Finally, further validation was carried out in the ICGC dataset.

Establish and evaluate a nomogram
Using the R package "rms", a probabilistic model was constructed to predict 1-, 3-, and 4-year survival in combination with age, gender, tumor grade, tumor stage, and LRRS.Simultaneously, calibration curves were plotted to evaluate the prediction accuracy of the nomogram.According to the C-index, the accuracy between nomogram and other prognostic factors was also assessed 27 .Additionally, the decision curve analysis (DCA) was conducted by the "DCA" package to measure the net clinical benefits of various forecasting models 28 .

Functional enrichment analyses
As described above, DEGs between the LRRS subgroups were isolated using the same protocol.Then, GO and KEGG analysis was performed by the "clusterProfiler" R package.After that, the GESA analysis was carried out using the Hallmark and C2 KEGG gene sets v7.4,which were used in conjunction with the GSEA software (version 4.1.0),with p < 0.05 and a FDR of < 0.25 were considered statistically significant 29 .

Stemness and Immune landscapes analyses
Stemness analysis was performed according to the previous report 30 .For tumor microenvironment analysis, the "estimate" R package was used to calculate the ImmuneScore and StromalScore in TCGA cohort 1 .The TME score was calculated as described previously 31 .The infiltration abundance of 24 immune cells of each HCC cancer sample was estimated by IMMUNCELL AI algorithm 32 .The numbers of 22 tumor-infiltrating immune cell (TIIC) from each sample were determined by using the package "CIBERSORT" (R) 33 .

Somatic mutations landscapes analyses
Somatic mutation data in "maf " format were downloaded from TCGA GDC data portal 34 , and waterfall plots were then visualized using the "maftools" package in R. Scores for tumor mutational burden (TMB) 35 and mutant allele tumor heterogeneity (MATH) 36 were calculated by the "maftools" package in R.

Prediction of treatment sensitivity
The tumor immune dysfunction and exclusion (TIDE) was calculated to assess the immunotherapy responses in TCGA and validated in the ICGC cohort, as described previously 37 .The cancer-related chemotherapeutic drug sensitivity was predicted via the Genomics of Drug Sensitivity Database following the previous study 38 .

Single-cell RNA sequencing analysis
Single-cell sequencing analysis methods were referenced from previously published study 39 .In short, sequencing data was downloaded from GEO(GSE149614) and processed by the "Seurat V4.0" R package.

Stratified analysis, independent prognostic analysis, and diagnostic analysis
To clarify the correlation between LRRS and clinical features, we further analyzed the differences in LRRS stratified by clinical characteristics in the various subgroups.As shown in Fig. 4A,B, there was no difference in the value of LRRS between the two groups in terms of age (p > 0.05) and gender (p > 0.05) groups in both training cohort and validation cohort.However, LRRS in advanced stage (III-IV) was apparently higher than that in the early stage (I-II) in both data sets (p = 0.003; p < 0.001).Likewise, values with LRRS in high grade (3 & 4) was remarkable higher than that for low grade (1 & 2) in TCGA cohort (p = 0.021).This demonstrated that the LRRS model had potential correlations with clinical stage and grade of HCC patients.In addition, HCC patients, all in the high LRRS subgroups, presented a poor OS in TCGA cohort (Figs.4C).In ICGC cohort, patients with high-LRRS also indicated shorter survival time in age (> 60) subgroup, Female subgroup, and stage 3-4 subgroup (Figs.4D).These findings indicated that our LRRS model maybe a universal applicability tool for prognostic screening.
we further evaluated whether LRRS signature may assist in more accurate diagnosis of HCC.The level of LRRS was found to increase with tumor stage, alluding to a possible novel biomarker for HCC (Fig. 5C, D).Then,  the diagnostic performance of the LRRS was evaluated using a ROC analysis.As shown in Fig. 5C, the LRRS vastly outperformed AFP in differentiating HCC from normal samples (AUC: 0.991 vs. 0.731).Likewise, the

Tumor stemness analyses
Studies have reported that tumor cell stemness-related indexes were supposed to be significantly corelated with drug resistance, cancer recurrence and proliferation, and a high index seems to be directly related to the progress of various types of cancer 30,40 .In addition, these index can also help to identify new targets of anti-cancer drugs.Results showed that the value of RNAss and LRRS were positively correlated (Fig. 7F), and the correlation between EREG.EXPss and LRRS was not statistically significant (Fig. 7H).At the same time, parients with high LRRS showed higher values of RNAss (Fig. 7G, p = 0.013) and EREG.EXPss (Fig. 7I, p = 0.026) than that of the low-LRRS group.

Immune landscapes analyses
According to the GSEA analysis, we found immune-related pathways(leukocyte transendothelial migration, B-cell and T-cell receptor signaling pathways) were significantly enriched in the low LRRS group (Fig. 9A).Interestingly, samples in low-LRRS exhibited significantly higher StromalScore, compared with that of high-LRRS group (Fig. 9B, p < 0.05), as were the TME score (Fig. 9F, p < 0.001).However, both groups were not statistically different in ImmuneScore (Fig. 9D, p > 0.05).Furthermore, the correlation of the three scores and LRRS was also explored.The results showed that except for ImmuneScore, both StromalScore and TME score were significantly negatively correlated with LRRS(Fig.9C, E, G).Then, based on the TCGA cohort,the infiltration level of 24 immune cells were evaluated using ssGSEA by the ImmuneCellAI online tool.Surprisingly, both groups were not statistically different in the infiltration of most immune cells, including, NK cells, CD4+ T cells, neutrophils, B cells,Th2, and cytotoxic cells, while patients in the low-LRRS group showed a higher fraction of anti-tumor immune cells, such as CD8-naive (p < 0.05), Th17 cells (p < 0.01), and Monocyte (p < 0.001) (Fig. 9H).Moreover, a higher level of exhausted T (p < 0.05) (a group of T cells that have reduced effector function and continue to express inhibitory receptors), Th1 (p < 0.05), NKT (p < 0.001), DC (p < 0.001), CD8+ T (p < 0.01) and nTreg (p < 0.01) were observed in the high-LRRS group (Fig. 9H).Furthermore, we used the CIBERSORT algorithm to verify the infiltration level of immune cells in TCGA cohort and found that the low-LRRS group shown a higher infiltration of a variety of anti-tumor immune cells, including B naive cells (p < 0.05), CD4+ T memory resting cells (p < 0.05), Monocytes (p < 0.05), and Mast resting cells (p < 0.05), while high-LRRS group showed a higher estimated proportion of tumor-promoting cell, Tregs (p < 0.01) (Fig. 9I).At the same time, we found that the high LRRS group showed a high proportion of M0 infiltration (p < 0.001) (Fig. 9I).It is well known that the www.nature.com/scientificreports/elevation of M0 macrophages may represent some adverse immune response, such as autoimmune diseases or cancer.This is highly consistent with the results of ssGESA.All these data indicated that the LRRS is involved in the regulation of immune microenvironment and may affect the anti-tumor immune response in tumors.

Role of LRRS in clinical decision-making
Given the difference in the TME between the high and low LRRS groups, TIDE algorithm was subsequently applied to predict patients' response to immunotherapy.Previous studies have reported that higher TIDE scores were associated with poorer response to immune checkpoint blocking therapy (ICB) and shorter survival www.nature.com/scientificreports/after ICB treatment 37 .Our results showed that the high-LRRS group had a higher TIDE score in the TCGA dataset(Fig.10A, p < 0.001), which was confirmed in the ICGC dataset(Fig.10B, p = 0.005).Furthermore, subclass www.nature.com/scientificreports/mapping results indicated that low-LRRS goup showed a more sensitive immunotherapy response in bothTCGA and ICGC cohorts (Fig. 10C, D, all p < 0.001).Moreover, based on IC50 values, the sensitivity of four common chemotherapy drugs was further analyzed.we discovered that the high-LRRS group was sensitive to all four drugs (Sorafenib, Paclitaxel, Gemcitabine, and 5-Fluorouracil) (Fig. 10E-H, all p < 0.001).In conclusion, these findings indicated that LRRS was is a feasible tool to instruct clinical treatment decisions of HCC patients.

CLN3, GBA, and LAPTM4B may be novel biomarkers in Hepatocytes
To further elucidate the special role of LRRS gene signature in HCC progression, single-cell RNA sequencing analysis was performed to investigate the expression profiles of LRRSs in the liver tumor microenvironment.Firstly, 192,675 cells from 10 primary liver tumors and 23,277 cells from 8 non-tumor liver tissues were obtained after quality control filtering.Afterward, these cells were merged, clustered, and annotated.Finally, these cells were mapped to B cells, Endothelial cells, Hepatocytes, Macrophage, Monocyte, NK cells, Smooth muscle cells, Dendritic cells,Tissue stem cells and T cells based on cell-type-specific marker genes (Fig. 11A, B).Remarkedly, the cell types differ greatly amongst tumor and non-tumor tissues (Fig. 11A, B).Subsequently, we mapped the expression landscape of 8 lysosomes-related genes, including CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B.As shown in Fig. 11C, D, BSG and ANXA2 were widely expressed in almost all clusters, implying their essential role in cell viability.Meanwhile, CTSA were highly expressed in Macrophage and Hepatocytes.Of note, CLN3, GBA, and LAPTM4B were specially expressed in Hepatocytes, which may be novel biomarkers for liver cancer Hepatocytes(Fig.11C).Given the high specificity of CLN3, GBA, and LAPTM4B in Hepatocytes, we next elucidated their role in Hepatocytes function.The Hepatocytes were classified into nine subpopulations via dimensionality reduction (Fig. 11E).Notably, CLN3 and GBA were highly expressed in Hepatocytes (6), and LAPTM4B was highly expressed in Hepatocytes (3) (Fig. 11F, G).Furthermore, functional enrichment analysis of Hepatocytes ( 6)-specific genes revealed strong enrichment of protein secretion, G2M checkpoint, E2F pathways, Wnt/β-catenin signaling and mitotic spindle.Moreover, LCSC (3) strong enrichment of cancer hall markers related to Notch signaling, Glycolysis, PI3K/AKT/mTOR signaling, TGF-βsignaling, Angiogenesis, P53 pathway and Hypoxia (Fig. 11H).Therefore, CLN3, GBA, and LAPTM4B may be involved in cancer progression of liver cancer hepatocytes.

The expression confirmation of model genes
Compared with normals, all eight genes were highly expressed in HCC in GSE144269 (Fig. 12A, all p < 0.001), and which was validated using data from the GSE76427 dataset(Fig.12B, all p < 0.001) Subsequently, the protein expression of these genes were analyzed in the HPA database.According to the results, the protein expression of model genes in tumor tissue were elvated (Fig. 12C).Furthermore, the relative expression of model genes were also confirmed in HCC cell lines.Compared with normal cell line(LO2), the model genes were highly expressed in multiple tumor cells (Fig. 12D-G).Overall, these results further validated the stability and reliability of the LRRS model.

Discussion
Previous studies have indicated that lysosomal-related genes may serve as potential targets for cancer therapy 14,16,41 .However, the clinical relevance of lysosomal-related genes in the diagnosis and treatment of primary liver cancer has not been fully elucidated.In this study, we demonstrated the key role of lysosomal-related genes (LRGs) in HCC through functional enrichment analysis of differentially expressed genes.Subsequently, we identified ten LRRGs that were overexpressed in HCC and associated with poor prognosis.Furthermore, we constructed a panel of eight LRRGs that exhibited good performance in the diagnosis and prognosis of HCC patients.In summary, the comprehensive transcriptomic analysis of lysosomal-related genes in this study provides insights into the role of lysosomes in HCC (Fig. 13).
Due to the vital role played by lysosomes in cancer, a LRRS signature was constructed, including 8 genes, namely, CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B.Combined with literature reports and our analysis, all eight model genes were abnormally high expressed in HCC.Ceroid-lipofuscinosis 3(CLN3), encodes a lysosomal transmembrane protein, which functions as a necessary clearance enzyme for lysosome to clear glycerophosphate diesters (GPDs) 42 .By activating the EGFR/PI3K/AKT pathway, its upregulation leads to tumor growth and metastasis in HCC 22 .Glucosylceramidase (GBA) is considered as a necessary enzyme for autophagic degradation 43 and has been linked to a variety of cancers in humans [44][45][46] .A recent study found that abnormally elevated GBA is correlates with HCC invasion and poor survival,which further showed that artesunate(ART), an anti HCC drug, achieved its anti-tumor effect through the accumulation of GBA targeted autophages 21 .According to a previous research, LAPTM4B promotes tumor growth and autophagy in HCC cells by activating ATG3 transcription 20 .More importantly, our study revealed for the first time that CLN3, GBA, and LAPTM4B are specifically expressed in hepatocytes in the liver and promote the progression of liver cancer through multiple tumor-related pathways.This further suggests a potential link between lysosomal-related genes and the occurrence and development of liver cancer.Understanding the molecular mechanisms of CLN3, GBA, and LAPTM4B in liver cancer cells may help to develop new therapeutic targets for liver cancer.In addition, Cathepsin A (CTSA), a lysosome-encapsulated cellular proteases, its abnormal expression promotes tumor growth and metastasis 23,24.Zhao et al. 47 also reported that CTSA was overexpressed and associated with the carcinogenesis of liver cancer.BSG also known as Basigin or CD147, EMMPRIN, an immunoglobulin member, which can interact with extracellular, intracellular and membrane proteins and is the first reported protein to promote cancer development 48 .A recent study using CD147-CAR immunotherapy to treat HCC based on the abnormal high expression of CD47 and its negative correlation with prognosis 49 .Apelin (APLN) encodes an adipokine prepropeptide.Muto et al. 50disclosed that APLN overexpression was associated with arteriogenesis in HCC.In addition, through activation of the PI3K/Akt pathway, APLN regulates the progression of HCC 51 .As a lipid metabolism regulatory gene, Sort1 participated in the LDL metabolism and largely involved in the directional transport of various proteins in lysosomes 52,53 .Recent study reported that Sort1 exerted its function as pro-oncogenic molecules in HCC 54 .Studies have found that Annexin A2 (anx2) is related to tumor migration, epithelial mesenchymal transformation (EMT) and promotes tumor progression 55 .As the previous report, lysosome is significantly associated with cancer cell proliferation, invasion, metastasis, and gene expression regulation 56 .Consistent with the aforementioned reports, our results indicate that these 8 gene signatures are closely associated with malignant clinical features and immune therapy resistance in liver cancer.Furthermore, our results also suggest that these 8 gene signatures can independently predict overall survival outcome apart from known clinical and pathological risk factors.Additionally, we observed that all 8 model genes play a crucial role in the progression and development of tumors through the regulation of lysosomal-related pathways.Recently, a prognostic model of related lysosome-related genes has also been reported 41 .The authors used 8 genes (RAMP3, GPLD1, FABP5, CD68, CSPG4, SORT1, CSPG5, CSF3R) to construct a risk model, and the study showed that the risk model could better predict the clinical outcome, and the higher the risk, the worse the clinical outcome.In addition, the authors found significant differences in biological function, immune microenvironment, immunotherapy responsiveness and drug sensitivity between high-risk group and low-risk group.In terms of research content and conclusion, our study and the above study focus on lysosome-related genes and their relationship with hepatocellular carcinoma (HCC), with the purpose of identifying prognostic markers and evaluating their potential impact on the diagnosis, prognosis and treatment of HCC.At the same time, both studies found significant differences in clinical outcomes between high-risk and low-risk groups, that is, the high-risk group had worse clinical outcomes than the low-risk group, indicating the potential utility of the identified genetic signatures as prognostic markers.However, there are several differences between the two studies.First, there are differences in the specific lysosomal-associated genes that were identified as significant and used in risk modeling between the two studies, which may be related to the different gene sets that were included in the analyses.Second, the former study evaluated the differences in biological function, immune microenvironment, and drug sensitivity between high and low risk groups.However, our study evaluated tumor stemness, heterogeneity, genomic alteration status, immune-cell infiltration, and response to immunotherapy and chemotherapy.In addition, our study more comprehensively evaluated the early diagnostic value of risk www.nature.com/scientificreports/Currently, AFP is still the most commonly used non-invasive diagnostic marker for HCC, but its diagnostic sensitivity and specificity are still relatively low 57 .It is noteworthy that the performance of our 8-gene signature in distinguishing HCC patients from normal samples and early-stage liver cancer is superior to AFP..In this study, HCC patients with high LRRGs scores appear to be more sensitive to common clinical chemotherapy drugs, such as Sorafenib, Paclitaxel, Gemcitabine, and 5-Fluorouracil for liver cancer, illustrating that LRRS maybe a potentially tool for drug sensitivity prediction.In addition, considering the impact of LRRS on the clinical outcomes, a nomogram including LRRS, clinical features was constructed,which had a excellent predictive www.nature.com/scientificreports/value for HCC.Therefore, the LRRGs signature constructed in this study may be a promising biomarker for the diagnosis and prognosis of HCC.It has been reported that the number of lysosomes in the lysosomal network affects cell growth by activating mTOR protein 58 .When the number of lysosomes increases, the mTOR molecule on the surface of the lysosomal body becomes hyperactive.The GO, KEGG, and GSEA analysis may explain the causes of prognostic differences between the LRRS-classified HCC groups.Multiple immune-related pathways were also found to be enriched in low-LRRS group, such as T/B cell receptor signaling pathways and leukocyte transendothelial migration.It indicated that low-LRRS patients with higher immune activity might have a better prognosis.Immune cell infiltration is an indirect manifestation of immune activity.It has been reported that CD8 + T cells can induce anti-tumor response by producing interferon-(IFN) 59 .Th17 cells are considered to have high and long-term efficacy antitumor activity 60,61 .Interestingly, Treg cells can suppress immune activation by secreting immunosuppressive factors or expressing co inhibitory molecules 62 .As mentioned earlier, lysosomes are critically involved in tumor immunity.In our study, exhausted T and nTreg infiltrated high-LRRS group more than low-LRRS group.On the contrary, CD8-naive, Th17 cells, and Monocyte infiltrated low-LRRS group more than high-LRRS group.All these result suggestted that high-LRRS may relate to immunosuppression, and associate with a poor prognosis,while low-LRRS maybe relate to immune activity and achieve a well prognosis.Nowadays, a series of targeting drugs have been developed for HCC such as, anti-PD-1, anti-PD-L1 and anti-CTLA-4 63 .However, some success has been reported with immunotherapy in the treatment of HCC, the number of people who benefited from immunotherapy is still very low.Therefore, pre-treatment evaluation is particularly necessary.Our study showed a positive correlation between LRRS and TME scores, which provides a possibility for the prediction of immunotherapy.Predictably, TIDE scores did differ between the two LRRS groups and patients with low LRRS were more sensitive to immunotherapy, which is highly consistent with our analysis.It is further confirmed that LRRS still has potential value in predicting the efficacy of in tumor ICI therapy.
Tumor heterogeneity and stemness are strongly associated with the choice of cancer treatment and the length of overall survival time 64 .Our study revealed that LRRS was positively related to the tumor stemness and tumor heterogeneity, which mean that HCC cells with higher LRRS are more primitive and less differentiated.TMB is an important marker for predicting cancer efficacy, especially for immunotherapy 65 .Previous study reported that thymic epithelial tumors patients with high TMB had a significantly poor prognosis.We found no difference in TMB between the two groups, but regarding somatic mutation, significantly higher population mutation rate were observed in patients with high-LRRS, which may also indicate the poor prognosis in the high-LRRS group.
In summary, our study systematically analyzed and obtained the potential clinical value of lysosomal-related genes in HCC.Firstly, we revealed the aberrant expression profiles of lysosomal-related genes in HCC, confirming their pro-cancer role in HCC.Secondly, we constructed a lysosomal-related gene signature consisting of CLN3, GBA, CTSA, BSG, APLN, SORT1, ANXA2, and LAPTM4B, which demonstrated high performance in the diagnosis and prognosis of HCC patients.Additionally, this LRRGs signature was strongly associated with clinical features of malignant tumors, immune-suppressive tumor microenvironments, and chemotherapy response.Finally, the specific expression of CLN3, GBA, and LAPTM4B in Hepatocytes suggested their potential as biological markers for liver cells.In conclusion, the systematic evaluation of lysosomal-related genes in HCC can provide theoretical basis for their clinical application, help us understand the occurrence of liver cancer, and accelerate the development of new intervention strategies.However, our study also has some limitations.Firstly, although the results have been validated through multiple approaches, further clinical multicenter validation is still needed.Secondly, the specific mechanisms and roles of CLN3, GBA, and LAPTM4B in liver cells require further investigation.Thirdly, the potential mechanisms of lysosomes in chemotherapy response and immunesuppressive tumor microenvironment need further exploration. https://doi.org/10.1038/s41598-023-49985-3

Figure 1 .
Figure 1.Identification of the lysosomes-related differentially expressed genes (DELRGs).(A) Visualization of the differentially expressed genes (DEGs) in TCGA using a volcano plot.(B) Overlapping representation of the DEGs and lysosomes-related genes (LRGs) in a Venn diagram.The Venn diagram of the DEGs and LRGs.(C) The heat map of 15 DELRGs between HCC and normal tissues in TCGA.(D) The Pearson correlation analysis of the DELRGs in TCGA.(E-G) The function of the DELRGs in the Metascape database.

Figure 2 .
Figure 2. Construction and evaluation of the Lysosome-related risk score (LRRS).(A) Univariate Cox regression analysis was performed to assess the predictive value of the 15 DELRGs in the TCGA cohort.(B) Construction of the LASSO model.(C) The optimal λ value, determining the regularization strength of the LASSO model, was identified for the selected eight LRRS-related genes.(D) The risk factor diagram of LRRS model in TCGA cohort was generated, illustrating the significance of the selected genes in predicting risk.(E) The overall survival (OS) curve was plotted to compare the outcomes between high-and low-LRRS groups in the TCGA cohort, demonstrating the prognostic value of the LRRS model.Principal component analysis (PCA) (F), t-distributed Stochastic Neighbor Embedding (t-SNE) (G), and Uniform Manifold Approximation and Projection (UMAP) (H) were utilized to visualize the LRRS subgroup plot of the LRRS subgroup, portraying its distinct characteristics.(I) The 1-, 3-, and 4-year receiver operating characteristic (ROC) curves were constructed to evaluate the performance of the LRRS model in predicting survival outcomes in the TCGA cohort.

Figure 3 .
Figure 3. Validation of the LRRS model in the ICGC cohort.(A) Risk plot distribution and survival status.(B) Kaplan-Meier curves for the OS.(C) PCA, (D) UMAP, and (E) t-SNE plot of the risk model.(F) 1-, 3-, and 4-year ROC curves of LRRS model for survival prediction in ICGC cohort.

Figure 4 .
Figure 4. LRRS model-based stratified survival analysis of clinical features in TCGA cohort and validated in ICGC LIRI-JP cohort.Comparison of differences in LRRS between groups based on the clinical parameters of age, gender, stage, and grade using the Wilcoxon signed-rank test in (A) TCGA cohort and validated in (B) ICGC cohort.Survival analysis of OS stratified by LRRS and HCC clinical parameters in(C) TCGA cohort and validated in (D) ICGC cohort.

Figure 5 .
Figure 5. Cox regression analysis and diagnostic analysis of the LRRS signature in HCC.The univariate and multivariate Cox regression analyses in (A) TCGA cohort and in (B) ICGC cohort.(C).The value of the LRRS in different groups, including normal (n = 50), and HCC tissues at different stages (stages I, n = 177; stages II, n = 88; stages III, n = 86; stages IV, n = 5); ROC curves and AUC values for the LRRS and AFP to distinguish HCC from normal, and to differentiate between normal and patients with early stage (stages I & II) of HCC.(D) Diagnostic performance was further validated in ICGC (normal, n = 202; stages I, n = 36; stages II, n = 105; stages III, n = 71; stages IV, n = 19).LRRS levels (mean ± SEM) among multiple groups were statistically analyzed by Ordinary one-way ANOVA. https://doi.org/10.1038/s41598-023-49985-3

Figure 7 .
Figure 7. Functional enrichment and stemness analyses between the high and low LRRS groups.(A) Displays volcanic map of DEGs observed in the high and low LRRS groups.The GO analysis (B) and KEGG analysis (C) diagram present the enriched pathways of the DEGs.The GSEA results of for KEGG pathways in the high-LRRS (D) and low-LRRS (E) groups are illustrated.(F) Correlation scatter plot of LRRS and RNAss.(G) The violin plot of the difference in RNAss between the high and low LRRS groups.(H) Correlation scatter plot of LRRS and EREG.EXPss.(I) The violin plot of the difference in EREG.EXPss between the high and low LRRS groups.

Figure 8 .Figure 9 .Figure 10 .Figure 11 .
Figure 8.Comparison of somatic mutations between LRRS-based groups.Waterfall maps of mutated genes in HCC patients with high LRRS (A) and low LRRS (B) groups.(C) Forest maps of differentially mutated genes in patients with high LRRS and low LRRS HCC.Comparison of TMB (D) and MATH score (E) between HCC patients with high and low LRRS.Data were analyzed by Wilcoxon test. https://doi.org/10.1038/s41598-023-49985-3

Figure 13 .
Figure 13.Workflow of this study.